Open Access

REVIEW

Resilient Photovoltaics: Global Optimization and Advanced Control under Complex Operating Conditions: A Critical Review

Wulfran Fendzi Mbasso^1,2, Idriss Dagal³, Manish Kumar Singla^4,5,*, Muhammad Suhail Shaikh⁶, Aseel Smerat⁷
1 Department of Biosciences, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Chennai, 602105, India
2 Applied Science Research Center, Applied Science Private University, Amman, 11931, Jordan
3 Electrical Engineering, Beykent University, Ayazağa Mahallesi, Hadım Koruyolu Cd. No:19, Sarıyer, İstanbul, 34398, Turkey
4 Chitkara University Institute of Engineering & Technology, Chitkara University, Punjab, 140401, India
5 Jadara University Research Center, Jadara University, P.O. Box 733, Irbid, 21110, Jordan
6 School of Physics and Electronic Engineering, Hanshan Normal University, Chaozhou, 521041, China
7 Faculty of Educational Sciences, Al-Ahliyya Amman University, Amman, 19328, Jordan
* Corresponding Author: Manish Kumar Singla. Email:
(This article belongs to the Special Issue: Global Intelligent Optimization and Advanced Control of Photovoltaic Systems Under Complex Operating Conditions)

Energy Engineering https://doi.org/10.32604/ee.2026.072899

Received 06 September 2025; Accepted 03 November 2025; Published online 29 January 2026

Abstract

Utility-scale PV plants increasingly operate under partial shading, soiling, temperature swings, and rapid irradiance ramps that depress yield and challenge stability on weak grids. This critical review addresses those conditions by (i) unifying a stressor-to-method taxonomy that links field stressors to global intelligent MPPT (metaheuristics and learning-based trackers) and to advanced inverter controls (adaptive/MPC and grid-forming), (ii) standardizing metrics and reporting aligned with IEC 61724-1 and IEEE 1547/1547.1 to enable fair, reproducible comparisons, and (iii) framing MPPT and grid support as a co-design problem with a DT→HIL→Field validation pathway and seedable scenarios. We identify persistent gaps—fragmented partial-shading benchmarks, limited low-SCR testing, and scarce field-grade validation—and compile a quantitative synthesis: global soiling typically reduces annual production by ≈3%–5%, and hybrid/learning MPPT frequently report ≈99% tracking efficiency under PSC in simulation/HIL studies. To demonstrate practical relevance, we validate the framework on a seeded scenario library: DRL trackers achieve median η_MPPT ≈ 0.996 with t₉₅ ≈ 0.19 s and Hybrid trackers ≈0.992/0.26 s, outperforming Metaheuristics (≈0.984/0.42 s); at SCR = 2.5, grid-forming control raises VRI from ~0.78 (tuned GFL) to ~0.95 while keeping THD within 2.5%–3.2%, with all stacks meeting IEEE-1547.1 Category-II ride-through. The resulting taxonomy, standards-aligned reporting, and open seeds provide a replicable basis for comparable, grid-relevant benchmarking and clear guidance for real-world design and operations.

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Graphical Abstract

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Keywords

Photovoltaic (PV) systems; intelligent optimization; maximum power point tracking (MPPT) under partial shading; grid-forming control; weak-grid resilience

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